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Monotone Quantifiers Emerge via Iterated Learning
Author(s) -
Carcassi Fausto,
SteinertThrelkeld Shane,
Szymanik Jakub
Publication year - 2021
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1111/cogs.13027
Subject(s) - monotonic function , problem of universals , linguistic universal , computer science , iterated function , meaning (existential) , perspective (graphical) , artificial intelligence , semantic property , monotone polygon , theoretical computer science , mathematics , linguistics , epistemology , theoretical linguistics , philosophy , geometry , mathematical analysis
Natural languages exhibit many semantic universals , that is, properties of meaning shared across all languages. In this paper, we develop an explanation of one very prominent semantic universal, the monotonicity universal. While the existing work has shown that quantifiers satisfying the monotonicity universal are easier to learn, we provide a more complete explanation by considering the emergence of quantifiers from the perspective of cultural evolution. In particular, we show that quantifiers satisfy the monotonicity universal evolve reliably in an iterated learning paradigm with neural networks as agents.